Mixed-effects regression and eye-tracking data Lecture 2 of advanced - PowerPoint PPT Presentation

Mixed-effects regression and eye-tracking data Lecture 2 of advanced regression methods for linguists Martijn Wieling and Jacolien van Rij Seminar für Sprachwissenschaft University of Tübingen LOT Summer School 2013, Groningen, June 25 1 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Today’s lecture ◮ Introduction ◮ Gender processing in Dutch ◮ Eye-tracking to reveal gender processing ◮ Design ◮ Analysis ◮ Conclusion 2 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Gender processing in Dutch ◮ The goal of this study is to investigate if Dutch people use grammatical gender to anticipate upcoming words ◮ This study was conducted together with Hanneke Loerts and is published in the Journal of Psycholinguistic Research (Loerts, Wieling and Schmid, 2012) ◮ What is grammatical gender? ◮ Gender is a property of a noun ◮ Nouns are divided into classes: masculine, feminine, neuter, ... ◮ E.g., hond (‘dog’) = common, paard (‘horse’) = neuter ◮ The gender of a noun can be determined from the forms of other elements syntactically related to it (Matthews, 1997: 36) 3 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Gender in Dutch ◮ Gender in Dutch: 70% common, 30% neuter ◮ When a noun is diminutive it is always neuter ◮ Gender is unpredictable from the root noun and hard to learn ◮ Children overgeneralize until the age of 6 (Van der Velde, 2004) 4 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Why use eye tracking? ◮ Eye tracking reveals incremental processing of the listener during the time course of the speech signal ◮ As people tend to look at what they hear (Cooper, 1974), lexical competition can be tested 5 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Testing lexical competition using eye tracking ◮ Cohort Model (Marslen-Wilson & Welsh, 1978): Competition between words is based on word-initial activation ◮ This can be tested using the visual world paradigm: following eye movements while participants receive auditory input to click on one of several objects on a screen 6 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Support for the Cohort Model ◮ Subjects hear: “Pick up the candy” (Tanenhaus et al., 1995) ◮ Fixations towards target (Candy) and competitor (Candle): support for the Cohort Model 7 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Lexical competition based on syntactic gender ◮ Other models of lexical processing state that lexical competition occurs based on all acoustic input (e.g., TRACE, Shortlist, NAM) ◮ Does gender information restrict the possible set of lexical candidates? ◮ I.e. if you hear de , will you focus more on an image of a dog ( de hond ) than on an image of a horse ( het paard )? ◮ Previous studies (e.g., Dahan et al., 2000 for French) have indicated gender information restricts the possible set of lexical candidates ◮ In the following, we will investigate if this also holds for Dutch with its difficult gender system using the visual world paradigm ◮ We analyze the data using mixed-effects regression in R 8 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Experimental design ◮ 28 Dutch participants heard sentences like: ◮ Klik op de rode appel (‘click on the red apple’) ◮ Klik op het plaatje met een blauw boek (‘click on the image of a blue book’) ◮ They were shown 4 nouns varying in color and gender ◮ Eye movements were tracked with a Tobii eye-tracker (E-Prime extensions) 9 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Experimental design: conditions ◮ Subjects were shown 96 different screens ◮ 48 screens for indefinite sentences ( klik op het plaatje met een rode appel ) ◮ 48 screens for definite sentences ( klik op de rode appel ) 10 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Visualizing fixation proportions: different color 11 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Visualizing fixation proportions: same color 12 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Which dependent variable? ◮ Difficulty 1: choosing the dependent variable ◮ Fixation difference between Target and Competitor ◮ Fixation proportion on Target - requires transformation to empirical logit, to ( y + 0 . 5 ) ensure the dependent variable is unbounded: log ( ( N − y + 0 . 5 ) ) ◮ ... ◮ Difficulty 2: selecting a time span ◮ Note that about 200 ms. is needed to plan and launch an eye movement ◮ It is possible (and better) to take every individual sampling point into account, but we will opt for the simpler approach here (in contrast to lecture 4) ◮ In this lecture we use: ◮ The difference in fixation time between Target and Competitor ◮ Averaged over the time span starting 200 ms. after the onset of the determiner and ending 200 ms. after the onset of the noun (about 800 ms.) ◮ This ensures that gender information has been heard and processed, both for the definite and indefinite sentences 13 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Independent variables ◮ Variable of interest ◮ Competitor gender vs. target gender ◮ Variables which could be important ◮ Competitor color vs. target color ◮ Gender of target (common or neuter) ◮ Definiteness of target ◮ Participant-related variables ◮ Gender (male/female), age, education level ◮ Trial number ◮ Design control variables ◮ Competitor position vs. target position (up-down or down-up) ◮ Color of target ◮ ... (anything else you are not interested in, but potentially problematic) 14 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Some remarks about data preparation ◮ Check if variables correlate highly ◮ If so: exclude one variable, or transform variable ◮ See Chapter 6.2.2 of Baayen (2008) ◮ Check if numerical variables are normally distributed ◮ If not: try to make them normal (e.g., logarithmic or inverse transformation) ◮ Note that your dependent variable does not need to be normally distributed (the residuals of your model do!) ◮ Center your numerical predictors when doing mixed-effects regression ◮ See previous lecture 15 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Our data > head (eye) Subject Item TargetDefinite TargetNeuter TargetColor TargetBrown TargetPlace 1 S300 appel 1 0 red 0 1 2 S300 appel 0 0 red 0 2 3 S300 vat 1 1 brown 1 4 4 S300 vat 0 1 brown 1 1 5 S300 boek 1 1 blue 0 4 6 S300 boek 0 1 blue 0 1 TargetTopRight CompColor CompPlace TupCdown CupTdown TrialID Age IsMale 1 0 red 2 0 0 44 52 0 2 1 brown 4 1 0 2 52 0 3 0 yellow 2 0 1 14 52 0 4 0 brown 3 1 0 43 52 0 5 0 blue 3 0 0 5 52 0 6 0 yellow 3 1 0 30 52 0 Edulevel SameColor SameGender TargetPerc CompPerc FocusDiff 1 1 1 1 40.90909 6.818182 34.090909 2 1 0 0 63.63636 0.000000 63.636364 3 1 0 0 47.72727 43.181818 4.545455 4 1 1 0 27.90698 9.302326 18.604651 5 1 1 0 11.11111 25.000000 -13.888889 6 1 0 1 23.80952 50.000000 -26.190476 16 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

Our first mixed-effects regression model # A model having only random intercepts for Subject and Item > model = lmer ( FocusDiff ~ (1|Subject) + (1|Item) , data=eye ) # Show results of the model > print ( model, corr=F ) [...] Random effects: Groups Name Variance Std.Dev. Item (Intercept) 22.968 4.7925 Subject (Intercept) 257.111 16.0347 Residual 3275.691 57.2336 Number of obs: 2280, groups: Item, 48; Subject, 28 Fixed effects: Estimate Std. Error t value (Intercept) 30.867 3.377 9.14 17 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

By-item random intercepts 18 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen

By-subject random intercepts 19 | Martijn Wieling and Jacolien van Rij Mixed-effects regression and eye-tracking data University of Tübingen